@article {Coninck813,
author = {Coninck, Arne De and Fostier, Jan and Maenhout, Steven and De Baets, Bernard},
title = {DAIRRy-BLUP: A High-Performance Computing Approach to Genomic Prediction},
volume = {197},
number = {3},
pages = {813--822},
year = {2014},
doi = {10.1534/genetics.114.163683},
publisher = {Genetics},
abstract = {In genomic prediction, common analysis methods rely on a linear mixed-model framework to estimate SNP marker effects and breeding values of animals or plants. Ridge regression{\textendash}best linear unbiased prediction (RR-BLUP) is based on the assumptions that SNP marker effects are normally distributed, are uncorrelated, and have equal variances. We propose DAIRRy-BLUP, a parallel, Distributed-memory RR-BLUP implementation, based on single-trait observations (y), that uses the Average Information algorithm for restricted maximum-likelihood estimation of the variance components. The goal of DAIRRy-BLUP is to enable the analysis of large-scale data sets to provide more accurate estimates of marker effects and breeding values. A distributed-memory framework is required since the dimensionality of the problem, determined by the number of SNP markers, can become too large to be analyzed by a single computing node. Initial results show that DAIRRy-BLUP enables the analysis of very large-scale data sets (up to 1,000,000 individuals and 360,000 SNPs) and indicate that increasing the number of phenotypic and genotypic records has a more significant effect on the prediction accuracy than increasing the density of SNP arrays.},
issn = {0016-6731},
URL = {http://www.genetics.org/content/197/3/813},
eprint = {http://www.genetics.org/content/197/3/813.full.pdf},
journal = {Genetics}
}